Overview

Dataset statistics

Number of variables9
Number of observations1096
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory85.6 KiB
Average record size in memory80.0 B

Variable types

Numeric9

Alerts

FREQUENCIA BOMBA 1 is highly overall correlated with FREQUENCIA BOMBA 2 and 7 other fieldsHigh correlation
FREQUENCIA BOMBA 2 is highly overall correlated with FREQUENCIA BOMBA 1 and 7 other fieldsHigh correlation
NIVEL DO RESERVATÓRIO - LT01 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
VAZÃO DE ENTRADA- FT01 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
VAZÃO DE GRAVIDADE - FT02 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
VAZÃO DE RECALQUE - FT03 is highly overall correlated with FREQUENCIA BOMBA 1 and 7 other fieldsHigh correlation
PRESSÃO DE SUCÇÃO - PT01 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
PRESSÃO DE RECALQUE - PT02 is highly overall correlated with FREQUENCIA BOMBA 1 and 6 other fieldsHigh correlation
FREQUENCIA BOMBA 3 is highly overall correlated with FREQUENCIA BOMBA 1 and 2 other fieldsHigh correlation
NIVEL DO RESERVATÓRIO - LT01 has unique valuesUnique
VAZÃO DE ENTRADA- FT01 has unique valuesUnique
VAZÃO DE GRAVIDADE - FT02 has unique valuesUnique
VAZÃO DE RECALQUE - FT03 has unique valuesUnique
PRESSÃO DE SUCÇÃO - PT01 has unique valuesUnique
PRESSÃO DE RECALQUE - PT02 has unique valuesUnique
FREQUENCIA BOMBA 2 has 125 (11.4%) zerosZeros
FREQUENCIA BOMBA 3 has 767 (70.0%) zerosZeros

Reproduction

Analysis started2022-11-29 23:13:59.416704
Analysis finished2022-11-29 23:14:24.240527
Duration24.82 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

FREQUENCIA BOMBA 1
Real number (ℝ)

Distinct1086
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.830497
Minimum0
Maximum57.884742
Zeros8
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2022-11-29T20:14:24.514371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.200479
Q150.491298
median54.203522
Q354.818361
95-th percentile55.871695
Maximum57.884742
Range57.884742
Interquartile range (IQR)4.327063

Descriptive statistics

Standard deviation12.354038
Coefficient of variation (CV)0.2529984
Kurtosis5.0811887
Mean48.830497
Median Absolute Deviation (MAD)0.97851443
Skewness-2.4214445
Sum53518.224
Variance152.62224
MonotonicityNot monotonic
2022-11-29T20:14:24.786215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 8
 
0.7%
10.45626259 2
 
0.2%
16.33021418 2
 
0.2%
6.707071304 2
 
0.2%
54.159537 1
 
0.1%
27.99845457 1
 
0.1%
49.66352145 1
 
0.1%
54.37093751 1
 
0.1%
54.06932179 1
 
0.1%
54.13174645 1
 
0.1%
Other values (1076) 1076
98.2%
ValueCountFrequency (%)
0 8
0.7%
0.07812268396 1
 
0.1%
0.1135240048 1
 
0.1%
1.457959493 1
 
0.1%
1.458722432 1
 
0.1%
1.874671936 1
 
0.1%
2.138453166 1
 
0.1%
2.416199684 1
 
0.1%
5.207150777 1
 
0.1%
5.62386322 1
 
0.1%
ValueCountFrequency (%)
57.88474162 1
0.1%
57.80188862 1
0.1%
57.78695647 1
0.1%
57.72492854 1
0.1%
57.67244562 1
0.1%
57.64668687 1
0.1%
57.41708151 1
0.1%
57.29679441 1
0.1%
57.24754477 1
0.1%
57.23739465 1
0.1%

FREQUENCIA BOMBA 2
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct972
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.538055
Minimum0
Maximum53.488396
Zeros125
Zeros (%)11.4%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2022-11-29T20:14:25.067055image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.288937
median19.964554
Q322.791507
95-th percentile28.666501
Maximum53.488396
Range53.488396
Interquartile range (IQR)9.5025693

Descriptive statistics

Standard deviation9.2819229
Coefficient of variation (CV)0.52924471
Kurtosis0.87398756
Mean17.538055
Median Absolute Deviation (MAD)4.0445266
Skewness-0.18571838
Sum19221.709
Variance86.154093
MonotonicityNot monotonic
2022-11-29T20:14:25.327909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 125
 
11.4%
22.60313161 1
 
0.1%
22.0105052 1
 
0.1%
21.64428806 1
 
0.1%
8.716883262 1
 
0.1%
21.35145617 1
 
0.1%
20.46317196 1
 
0.1%
18.66628154 1
 
0.1%
20.38431411 1
 
0.1%
19.8963955 1
 
0.1%
Other values (962) 962
87.8%
ValueCountFrequency (%)
0 125
11.4%
0.02895935582 1
 
0.1%
0.9161264896 1
 
0.1%
1.041552226 1
 
0.1%
1.104533116 1
 
0.1%
1.124928713 1
 
0.1%
1.249832153 1
 
0.1%
1.290262143 1
 
0.1%
1.301844279 1
 
0.1%
1.343984286 1
 
0.1%
ValueCountFrequency (%)
53.48839553 1
0.1%
52.76372433 1
0.1%
52.23364218 1
0.1%
50.31093295 1
0.1%
50.30937306 1
0.1%
50.05674442 1
0.1%
48.09578911 1
0.1%
47.95462648 1
0.1%
47.28153245 1
0.1%
46.25633566 1
0.1%

FREQUENCIA BOMBA 3
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct310
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2986567
Minimum0
Maximum46.841334
Zeros767
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2022-11-29T20:14:25.824624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.3758656
95-th percentile27.675307
Maximum46.841334
Range46.841334
Interquartile range (IQR)2.3758656

Descriptive statistics

Standard deviation8.9756603
Coefficient of variation (CV)2.0880151
Kurtosis3.8120072
Mean4.2986567
Median Absolute Deviation (MAD)0
Skewness2.1992915
Sum4711.3278
Variance80.562478
MonotonicityNot monotonic
2022-11-29T20:14:26.152436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 767
70.0%
9.664798737 18
 
1.6%
7.248599052 3
 
0.3%
4.832399368 2
 
0.2%
23.63431787 1
 
0.1%
25.701382 1
 
0.1%
26.63184325 1
 
0.1%
30.24013567 1
 
0.1%
26.63075733 1
 
0.1%
26.53662793 1
 
0.1%
Other values (300) 300
 
27.4%
ValueCountFrequency (%)
0 767
70.0%
0.003777491899 1
 
0.1%
0.01784148306 1
 
0.1%
0.02218056515 1
 
0.1%
0.02251497004 1
 
0.1%
0.02712027091 1
 
0.1%
0.04579362473 1
 
0.1%
0.04781429508 1
 
0.1%
0.05635319138 1
 
0.1%
0.0617486091 1
 
0.1%
ValueCountFrequency (%)
46.84133418 1
0.1%
44.46157153 1
0.1%
41.99538366 1
0.1%
40.82297285 1
0.1%
38.32062356 1
0.1%
36.97235439 1
0.1%
35.73849392 1
0.1%
35.44564088 1
0.1%
34.455494 1
0.1%
33.64279477 1
0.1%

NIVEL DO RESERVATÓRIO - LT01
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1096
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5445124
Minimum1.4588396
Maximum4.255611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2022-11-29T20:14:26.470253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.4588396
5-th percentile2.6396807
Q13.3451235
median3.6440865
Q33.842757
95-th percentile4.0744165
Maximum4.255611
Range2.7967713
Interquartile range (IQR)0.49763351

Descriptive statistics

Standard deviation0.43764183
Coefficient of variation (CV)0.12347025
Kurtosis1.8624724
Mean3.5445124
Median Absolute Deviation (MAD)0.23137046
Skewness-1.2605793
Sum3884.7856
Variance0.19153037
MonotonicityNot monotonic
2022-11-29T20:14:26.907005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.085867832 1
 
0.1%
3.745542735 1
 
0.1%
4.044858406 1
 
0.1%
3.92645368 1
 
0.1%
3.875668506 1
 
0.1%
3.984868606 1
 
0.1%
3.875286599 1
 
0.1%
3.5516828 1
 
0.1%
3.782529573 1
 
0.1%
3.434288959 1
 
0.1%
Other values (1086) 1086
99.1%
ValueCountFrequency (%)
1.458839649 1
0.1%
1.731062333 1
0.1%
1.836630325 1
0.1%
1.854625116 1
0.1%
1.921951505 1
0.1%
1.963316371 1
0.1%
1.973054121 1
0.1%
1.975009809 1
0.1%
2.047676899 1
0.1%
2.107955938 1
0.1%
ValueCountFrequency (%)
4.255610963 1
0.1%
4.238957544 1
0.1%
4.234671116 1
0.1%
4.22364532 1
0.1%
4.218583852 1
0.1%
4.203865339 1
0.1%
4.201150844 1
0.1%
4.19982486 1
0.1%
4.196092864 1
0.1%
4.194925229 1
0.1%

VAZÃO DE ENTRADA- FT01
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1096
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean211.77062
Minimum22.853874
Maximum301.86282
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2022-11-29T20:14:27.177853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum22.853874
5-th percentile112.36344
Q1195.94412
median215.7909
Q3238.32501
95-th percentile270.51985
Maximum301.86282
Range279.00895
Interquartile range (IQR)42.380893

Descriptive statistics

Standard deviation44.496078
Coefficient of variation (CV)0.2101145
Kurtosis3.2839285
Mean211.77062
Median Absolute Deviation (MAD)20.974483
Skewness-1.4394381
Sum232100.6
Variance1979.9009
MonotonicityNot monotonic
2022-11-29T20:14:27.455693image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
164.09419 1
 
0.1%
183.7702262 1
 
0.1%
217.3601926 1
 
0.1%
239.730663 1
 
0.1%
204.170449 1
 
0.1%
187.2899815 1
 
0.1%
233.0458415 1
 
0.1%
207.7327532 1
 
0.1%
209.3796951 1
 
0.1%
211.6505432 1
 
0.1%
Other values (1086) 1086
99.1%
ValueCountFrequency (%)
22.85387429 1
0.1%
23.67098368 1
0.1%
45.13681835 1
0.1%
45.76276124 1
0.1%
45.82522564 1
0.1%
46.14894212 1
0.1%
46.38032131 1
0.1%
53.48451341 1
0.1%
53.76006223 1
0.1%
55.46804079 1
0.1%
ValueCountFrequency (%)
301.8628197 1
0.1%
292.774423 1
0.1%
291.8711243 1
0.1%
291.7827042 1
0.1%
290.2942263 1
0.1%
289.9922231 1
0.1%
286.9505081 1
0.1%
286.6973852 1
0.1%
284.5989176 1
0.1%
284.3868116 1
0.1%

VAZÃO DE GRAVIDADE - FT02
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1096
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.80499
Minimum3.5839761
Maximum181.56548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2022-11-29T20:14:27.811489image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.5839761
5-th percentile71.409512
Q1105.09567
median115.73304
Q3126.24638
95-th percentile142.2588
Maximum181.56548
Range177.9815
Interquartile range (IQR)21.150711

Descriptive statistics

Standard deviation21.485903
Coefficient of variation (CV)0.18879579
Kurtosis2.9465071
Mean113.80499
Median Absolute Deviation (MAD)10.565998
Skewness-1.0775689
Sum124730.26
Variance461.64401
MonotonicityNot monotonic
2022-11-29T20:14:28.141300image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.5663263 1
 
0.1%
119.660035 1
 
0.1%
118.927659 1
 
0.1%
107.7398957 1
 
0.1%
108.8922803 1
 
0.1%
107.5259803 1
 
0.1%
101.8270885 1
 
0.1%
121.2368592 1
 
0.1%
110.7362493 1
 
0.1%
125.9014478 1
 
0.1%
Other values (1086) 1086
99.1%
ValueCountFrequency (%)
3.58397611 1
0.1%
15.88854376 1
0.1%
33.55565643 1
0.1%
34.30063144 1
0.1%
34.70834827 1
0.1%
35.52933693 1
0.1%
36.82452377 1
0.1%
37.51810519 1
0.1%
37.73271974 1
0.1%
38.05954901 1
0.1%
ValueCountFrequency (%)
181.565478 1
0.1%
177.4906346 1
0.1%
173.0618277 1
0.1%
169.2136116 1
0.1%
166.4727999 1
0.1%
165.6624734 1
0.1%
165.2773012 1
0.1%
165.0567973 1
0.1%
163.15286 1
0.1%
162.393026 1
0.1%

VAZÃO DE RECALQUE - FT03
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1096
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.13861
Minimum10.383355
Maximum143.98841
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2022-11-29T20:14:28.507092image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10.383355
5-th percentile44.08095
Q199.238742
median104.84557
Q3110.00566
95-th percentile118.0335
Maximum143.98841
Range133.60506
Interquartile range (IQR)10.766917

Descriptive statistics

Standard deviation19.821714
Coefficient of variation (CV)0.19794277
Kurtosis5.4573128
Mean100.13861
Median Absolute Deviation (MAD)5.3458869
Skewness-2.2501613
Sum109751.92
Variance392.90037
MonotonicityNot monotonic
2022-11-29T20:14:28.826912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78.38720433 1
 
0.1%
103.3230522 1
 
0.1%
99.73705737 1
 
0.1%
97.84226386 1
 
0.1%
98.00454283 1
 
0.1%
97.23211066 1
 
0.1%
95.51771514 1
 
0.1%
104.9044383 1
 
0.1%
98.95222044 1
 
0.1%
107.5846831 1
 
0.1%
Other values (1086) 1086
99.1%
ValueCountFrequency (%)
10.38335467 1
0.1%
18.78395883 1
0.1%
19.01510628 1
0.1%
19.22242113 1
0.1%
20.09405685 1
0.1%
20.10896885 1
0.1%
20.59290862 1
0.1%
21.63480695 1
0.1%
22.6513927 1
0.1%
22.69457825 1
0.1%
ValueCountFrequency (%)
143.9884148 1
0.1%
141.122522 1
0.1%
140.1231352 1
0.1%
137.250213 1
0.1%
137.2110043 1
0.1%
136.7754704 1
0.1%
136.7724743 1
0.1%
133.4322859 1
0.1%
133.0077931 1
0.1%
131.2276457 1
0.1%

PRESSÃO DE SUCÇÃO - PT01
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1096
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4852247
Minimum1.924965
Maximum5.4753866
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2022-11-29T20:14:29.143729image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1.924965
5-th percentile3.537572
Q14.2449013
median4.5707596
Q34.813108
95-th percentile5.119963
Maximum5.4753866
Range3.5504217
Interquartile range (IQR)0.56820674

Descriptive statistics

Standard deviation0.48689463
Coefficient of variation (CV)0.10855524
Kurtosis1.9289733
Mean4.4852247
Median Absolute Deviation (MAD)0.26363807
Skewness-1.1061814
Sum4915.8063
Variance0.23706638
MonotonicityNot monotonic
2022-11-29T20:14:29.706408image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.195563376 1
 
0.1%
4.652495493 1
 
0.1%
4.980844438 1
 
0.1%
4.872517745 1
 
0.1%
4.8248384 1
 
0.1%
4.934429506 1
 
0.1%
4.838139951 1
 
0.1%
4.440327237 1
 
0.1%
4.719303727 1
 
0.1%
4.318869789 1
 
0.1%
Other values (1086) 1086
99.1%
ValueCountFrequency (%)
1.924964962 1
0.1%
2.168335776 1
0.1%
2.599595224 1
0.1%
2.656243861 1
0.1%
2.743227368 1
0.1%
2.755907903 1
0.1%
2.801692863 1
0.1%
2.920023123 1
0.1%
2.923848917 1
0.1%
2.925910562 1
0.1%
ValueCountFrequency (%)
5.475386639 1
0.1%
5.469098727 1
0.1%
5.428008278 1
0.1%
5.420319339 1
0.1%
5.40986371 1
0.1%
5.409394821 1
0.1%
5.394903402 1
0.1%
5.376384755 1
0.1%
5.376057903 1
0.1%
5.329075277 1
0.1%

PRESSÃO DE RECALQUE - PT02
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct1096
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.424306
Minimum0.83083199
Maximum23.678933
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.1 KiB
2022-11-29T20:14:29.973254image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.83083199
5-th percentile5.6923869
Q119.805492
median21.015114
Q321.140136
95-th percentile21.809401
Maximum23.678933
Range22.848101
Interquartile range (IQR)1.3346445

Descriptive statistics

Standard deviation4.3233274
Coefficient of variation (CV)0.22257306
Kurtosis6.7578239
Mean19.424306
Median Absolute Deviation (MAD)0.24408048
Skewness-2.7339197
Sum21289.04
Variance18.69116
MonotonicityNot monotonic
2022-11-29T20:14:30.221113image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.06450653 1
 
0.1%
20.79550151 1
 
0.1%
19.6672767 1
 
0.1%
21.73330839 1
 
0.1%
20.97606885 1
 
0.1%
20.97365777 1
 
0.1%
21.27573272 1
 
0.1%
21.05959225 1
 
0.1%
19.4533143 1
 
0.1%
21.08179649 1
 
0.1%
Other values (1086) 1086
99.1%
ValueCountFrequency (%)
0.8308319853 1
0.1%
1.177278982 1
0.1%
1.503833353 1
0.1%
1.619448689 1
0.1%
1.921274748 1
0.1%
2.014124649 1
0.1%
2.069669306 1
0.1%
2.153511247 1
0.1%
2.236963258 1
0.1%
2.331033865 1
0.1%
ValueCountFrequency (%)
23.67893306 1
0.1%
23.64501381 1
0.1%
23.60372647 1
0.1%
23.59236177 1
0.1%
23.58846887 1
0.1%
23.56355182 1
0.1%
23.54971107 1
0.1%
23.50800292 1
0.1%
23.50494107 1
0.1%
23.50471417 1
0.1%

Interactions

2022-11-29T20:14:21.848895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:05.837051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:08.297632image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:10.002659image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:11.904573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:14.033358image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:15.784357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:17.737242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:19.581188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:22.046781image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:06.071904image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:08.532499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:10.193552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:12.117454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:14.202259image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:16.027220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:17.915141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:19.830047image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:22.233677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:06.287782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:08.721390image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:10.394434image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:12.344322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:14.385156image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:16.242096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:18.140013image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:20.087900image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:22.416571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:06.473675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:08.897289image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:10.592322image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:12.553202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:14.546064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:16.477961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:18.319909image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:20.314769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:22.604462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:06.670562image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:09.079188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:10.775220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:12.779074image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:14.723963image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:16.702834image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:18.500808image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:20.582618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:22.782362image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:06.906427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:09.260083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:10.992093image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:12.967969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:14.902860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:16.920709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:18.685702image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:21.027364image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:22.980247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:07.149288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:09.449974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:11.232955image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:13.166854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:15.132732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:17.136585image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:18.883588image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:21.237246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:23.178134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:07.547063image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:09.635868image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:11.466823image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:13.436697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:15.337611image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:17.340469image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:19.111455image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:21.432133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:23.408003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:08.046775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:09.816765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:11.699689image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:13.786500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:15.592468image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:17.557346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:19.339329image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-11-29T20:14:21.660002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-11-29T20:14:30.419000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-29T20:14:30.820771image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-29T20:14:31.160577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-29T20:14:31.481393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-29T20:14:31.801210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-29T20:14:23.700837image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-29T20:14:24.059633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

FREQUENCIA BOMBA 1FREQUENCIA BOMBA 2FREQUENCIA BOMBA 3NIVEL DO RESERVATÓRIO - LT01VAZÃO DE ENTRADA- FT01VAZÃO DE GRAVIDADE - FT02VAZÃO DE RECALQUE - FT03PRESSÃO DE SUCÇÃO - PT01PRESSÃO DE RECALQUE - PT02
Timestamp
2018-01-0145.7276170.9161260.0000004.085868164.09419096.56632678.3872045.19556316.064507
2018-01-0247.2393160.0000000.0000003.942272181.728099112.68885284.9235835.01749215.573998
2018-01-0348.1121570.0000000.0000003.883783193.140774113.66525087.0721104.94979115.761076
2018-01-0449.8694152.6819820.0000003.912685184.679417118.07626990.3885524.96143016.897601
2018-01-0537.03788619.4997528.5690133.389542211.898758125.74868091.4320634.43127417.123912
2018-01-0652.5756900.00000018.0761943.787355209.129580107.60639493.3965804.82300220.695101
2018-01-0748.3402350.0000005.7610714.002495195.95329494.77889581.3769225.10928117.836842
2018-01-0849.1390400.0000001.8828093.945957163.036853100.77853083.8518655.04404917.733694
2018-01-0949.9334600.0000000.0000003.875856187.854675100.26401885.7141874.95828717.919003
2018-01-1050.0779340.0000000.0000003.885556172.007514100.85445385.6511964.97100818.197270
FREQUENCIA BOMBA 1FREQUENCIA BOMBA 2FREQUENCIA BOMBA 3NIVEL DO RESERVATÓRIO - LT01VAZÃO DE ENTRADA- FT01VAZÃO DE GRAVIDADE - FT02VAZÃO DE RECALQUE - FT03PRESSÃO DE SUCÇÃO - PT01PRESSÃO DE RECALQUE - PT02
Timestamp
2020-12-2214.0389827.9044900.0000003.70034653.48451363.27256237.8284094.7944994.462368
2020-12-2357.08145135.9924060.0000002.545013260.922810152.082684116.4589363.37386121.444099
2020-12-2444.54935827.9934278.4239182.906787254.396596131.572730103.1638433.80320719.937727
2020-12-2552.12481419.1966900.0000003.958751244.321652113.99820492.0097394.93099919.936115
2020-12-2652.32372324.5718720.0000003.882665227.833759128.669075100.8638054.81296120.578570
2020-12-2750.53180913.2451380.0000004.139487221.111380125.41303190.9303705.12014418.466390
2020-12-2852.09169027.0434970.0000004.194925241.122370129.55073899.4529355.11915320.024225
2020-12-2952.19789627.2519140.0000004.013414209.975585125.79528399.2688924.94895020.154963
2020-12-3052.49404227.4913880.0000003.729430208.305455132.392801101.4722674.64374820.081885
2020-12-3145.61783227.0925837.0884653.775853226.161141124.05183197.1035594.71414920.237162